Deep Learning vs. Machine Learning– Deep Learning and Machine Learning are two of the key concepts of Artificial Intelligence. These technologies are also associated with the concept of data science. Due to the evolution in technology, ML, DL, and AI are trending, and the quest is to produce something that can help businesses and customers to overcome the existing problems. There is considerable demand for experienced machine learning development services.

Startups and companies across every industry are working to find ways to leverage machine learning in order to create new applications and systems that are more powerful, convenient, and efficient than anything before them. However, machine learning can be confusing at times, especially when it’s compared to other forms of artificial intelligence like deep learning. In this guide on the difference between machine learning and deep learning, we’ll outline what these terms mean and how they’re related so you can better understand their place in the future of computing.

To better understand the concept, it is necessary to know how machine learning and deep learning work? How are they related or not?. This blog is dedicated to revealing various aspects of all these concepts where significantly, we will see the difference between deep learning vs. machine learning.

 So, let’s start with the basic information.

 

Difference Between Machine Learning And Deep Learning

Machine learning and deep learning both are connected, there are some significant differences between them that you can find in the following table-

S.No.	Machine Learning	Deep Learning
1	ML is a subset of AI and a superset of Deep Learning	Deep learning is a subset of Machine Learning
2	Machine Learning demands manual feature extraction	It extracts the features and classifies its own
3	It uses a small amount of data	It requires large data sets.
4	Can work on the minimal hardware configuration	It required high-end machines for execution
5	Machine Learning takes place in smaller steps	Deep learning takes place on an end-to-end basis
6	Its execution time is more	It takes less time to execute
7	It produces only numerical values	It produces a variety of outputs from numerical values to texts, sounds, and images.
8	Refers to the general term where the computer learns from data	Refers to the mathematical complex evolution of machine learning.

 

What is Machine Learning?

Now the robots are not some filmy fantasy; these are the reality. There are several examples around us of it. In addition, you can also have a glimpse from newspapers and tech magazines. It is just because of the evolution of Artificial Intelligence. It is always the motive of AI to make machines smart and think like humans via learning. So, that is directly referring to deep learning and machine learning before we find out the real difference between deep learning and machine learning.

The official definition of Machine Learning can be understood as an application of AI that empowers the machine to learn by itself without any specific coding. The coding will be done only before implementing the system using AI components. In simpler words, machine learning is for developing the programs or applications that access the data to make the machine self learn. Regarding it, the role of a machine learning developer is crucial.

 

How does Machine Learning Work Exactly?

The human brain is designed to observe and learn things by itself, but a machine can’t. In real terms, the machine works specifically as it is designed. It takes the input and produces output based on it, followed by instructions given to it, But a human brain can’t.

There is no need to give instruction to the brain. It senses by itself, feels many things via the nervous system, and makes decisions. Although there is a difference between the human brain and other living beings, some basic functions perform similarly in humans and other creatures. For example, sensing the temperature of skin and others.

So, to make machines intelligent, there are certain tasks that need to be done. The research for making machines think is on rapid-fire, and we need to understand how it works.

Machine learning’s food is a training set that is made up of data. It is also called training data that is being used for knowledge mapping. It is nothing but only to find out the information for learning. Another crucial thing for machine learning is a knowledge graph. That makes machines understand the entities, domains, and connections.

The actual process of machine learning starts from the observation of the data, and that happens with direct experience and instruction. It forms inferences via looking at possible patterns in the data based on provided examples. The whole quest is to find the ways to make one that learns autonomously.

Practical Applications of Machine Learning

As per market statistics, the global AI market will reach $500 billion by 2024. Related to AI and machine learning IBM is a leading company having a 9% share in the global AI market. Surprisingly, IBM has more than 5500 machine learning and AI patents. The other runner-ups are Microsoft and Samsung. On the other hand, 57% of companies have invested in machine learning to improve the customer experience. That indicates AI-ML is such a fantastic technology that you can implement with the help of a Machine Learning Development Company.

 

 

The following are some best examples of practical applications of Machine Learning-

• Data Security

Data security is one of the prime concerns of many organizations worldwide. Any vulnerability can attract hackers to breach the systems. The machine learning models help identify the potential threats even before any hacking attack.

 

• Image Recognition

Image recognition has various applications and offers several advantages. It works super fantastic in combination with machine learning. With the help of AI-ML, the device can recognize the objects in front of the image and also find out the intensity of pixels. Image recognition can be used for crowd management, medical treatment, handwriting recognition, and more.

 

• Medical Diagnosis

Machine learning works as a boon for medical diagnosis. It uses healthcare datasets to find out the best way for diagnosis. ML helps in improving the patient’s experience, medical process automation to avoid human mistakes, finding out the cancerous tissues following the oncology and pathology principles, analyzing body fluids, and more.

 

• Fraud Detection

One of the top-notch features of Machine Learning is predictive analysis. First, it classifies the available data into the sets and then analyzes them to calculate the potential risks. For example- transaction prediction, prediction of possible faults, and more.

 

• Financial Predictions

Have you heard about Erica? If not, then you will be surprised to know that it is one of the finest examples of Chatbot Development. The Bank of America uses it for improving customer support.   

In this section of this blog, we have gone through all the information related to machine learning. Now, it is time to read something important about Deep Learning. So, let’s start with it.

 

What is Deep Learning?

First of all, let me clarify that it has close relation with machine learning and Artificial Intelligence. It includes predictive modeling. Like the AI-ML, its prime objective is to create human intelligence in a machine where sometimes deep learning exceeds it, making it one of the superior technology or one of the best options to implement with mobile app development. It works as one of the greatest elements of Data Science, where data scientists use it for collecting., analyzing, and interpreting large & complex sets of data. It makes work smooth and takes it further at a fast speed.

You will be surprised to know that deep learning-based computer models learn directly via classification, such as images, text, and sound. You can understand it as suppose your smart music system automatically creates the playlist and plays the songs you like most. It will analyze the data & generate patterns and then perform its task. These models are being trained with the help of neural networks.

 

How does Deep Learning work?

Before we proceed to find out the differences between deep learning and machine learning and the working of deep learning here, we need to understand neural networks.

So, its other names are ANN(Artificial Neural Network or Simulated Neural Networks); it is a subset of machine learning and works as a spinal cord for implementing deep learning models. The human brain inspires its name. It mimics how biological neurons connect and signal to each other.

 

 

The deep learning models can’t be possible without ANN; that is the reason these models are also called deep neural networks. The term “deep” refers to the existing hidden layers in the neural networks. An ordinary ANN only contains 2-3 hidden layers, but deep learning networks can contain more than 100-150 hidden layers. What differentiates it from Machine Learning.

The neural network works like a single neuron passes thousands of signals to the other, and it continues to produce the output. In a network, there are multiple layers that contain the nodes. So the signal travels from layer to layer and node to node with assigned weight. The role of the final layer is crucial. It compiles the weighted input for producing the output.

As you know, AI’s subset is Machine Learning, and Machine Learning’s subset is Deep Learning. So, a machine learning developer utilizes it for AI-based smart mobile app development.

 

Practical Applications of Deep Learning

Artificial Intelligence is progressing fast, and the near future is the self-driving car, which some the brands like Tesla have already implemented. Virtual assistants like Alexa, Google Assistant, or Siri are the reality, and almost all smartphone users are using these. These are becoming a part of our daily life. Deep learning has lots of possibilities to solve complex problems as other technologies can’t.

When it comes to finding out how machine learning helps improve the software development process, deep learning also makes the process smart to produce and deliver innovative software or mobile apps.

Here is some practical usage of Deep Learning that you would love to read about:

 

• Self Driving Vehicles

The concept of autonomous driving totally depends on Deep Learning. AI Scientists are trying to train the machine using millions of patterns and data so it can operate a vehicle safely. One of the best examples is UBER Artificial Intelligence Labs, which is trying to create autonomous vehicles that are not only used for commuting but can also perform the tasks such as food delivery.

 

• Fake News Detection

Almost all developed and developing nation governments are trying to get rid of the fake news that can spread many misconceptions and violence. The best remedy is Deep Learning Models that can identify the source and authenticity of the news. These models remove the fake news from feeds.

 

• Natural Language Processing

Have you ever used any language translation software such as Google Translate? But it has only limited capabilities, such as although it can transform the sentences in one language to another but can’t explain more. Suppose you want to see an English sentence in Arabic, then it will translate it into Arabic, but you can read it until you know the language.

 

• Virtual Assistants

Many users use virtual assistants such as Alexa or Siri. When you speak with these machines, they learn and analyze the voice patterns to produce the desired output. They take your commands as input and analyze them in the form of data sets. In the future, with the help of Depp Learning, virtual assistants will help you to perform multiple tasks.

 

• Fraud Detection

Although we are here to read the difference between Deep Learning vs Machine Learning, one thing that is common between them is to identify the unusual pattern that can be used for fraud detection. The deep learning algorithm helps identify customer transaction patterns and any abnormal activity with or within the user’s bank account.

 

The Final Thoughts

Both concepts belong to Artificial Intelligence. But suppose you are planning to use them for some implementation, suppose it is mobile app development for business. In that case, it is crucial to read and know about the technologies such as Deep Learning Vs Machine Learning. With the same motive, we have created this article that you may refer to for the reading and decide accordingly. The best way to implement these concepts is through Python Development services that you can get from some experienced AI development company.